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《小王子》多语言自然主义 fMRI 语料库。

Le Petit Prince multilingual naturalistic fMRI corpus.

机构信息

New York University Abu Dhabi, Neuroscience of Language Lab, Abu Dhabi, UAE.

Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, Hong Kong.

出版信息

Sci Data. 2022 Aug 29;9(1):530. doi: 10.1038/s41597-022-01625-7.

DOI:10.1038/s41597-022-01625-7
PMID:36038567
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9424229/
Abstract

Neuroimaging using more ecologically valid stimuli such as audiobooks has advanced our understanding of natural language comprehension in the brain. However, prior naturalistic stimuli have typically been restricted to a single language, which limited generalizability beyond small typological domains. Here we present the Le Petit Prince fMRI Corpus (LPPC-fMRI), a multilingual resource for research in the cognitive neuroscience of speech and language during naturalistic listening (OpenNeuro: ds003643). 49 English speakers, 35 Chinese speakers and 28 French speakers listened to the same audiobook The Little Prince in their native language while multi-echo functional magnetic resonance imaging was acquired. We also provide time-aligned speech annotation and word-by-word predictors obtained using natural language processing tools. The resulting timeseries data are shown to be of high quality with good temporal signal-to-noise ratio and high inter-subject correlation. Data-driven functional analyses provide further evidence of data quality. This annotated, multilingual fMRI dataset facilitates future re-analysis that addresses cross-linguistic commonalities and differences in the neural substrate of language processing on multiple perceptual and linguistic levels.

摘要

使用更具生态效度的刺激物(如有声读物)进行神经影像学研究,已经推进了我们对大脑中自然语言理解的认识。然而,先前的自然主义刺激通常仅限于单一语言,这限制了其在小类型学领域之外的通用性。在这里,我们介绍了 Le Petit Prince fMRI 语料库(LPPC-fMRI),这是一个用于研究自然聆听时言语和语言认知神经科学的多语言资源(OpenNeuro:ds003643)。49 名英语使用者、35 名汉语使用者和 28 名法语使用者在母语环境下聆听了同一本有声读物《小王子》,同时采集了多回波功能磁共振成像数据。我们还提供了时间对齐的语音注释和使用自然语言处理工具获得的逐字预测。结果表明,这些时间序列数据质量很高,具有良好的时间信噪比和高的受试者间相关性。数据驱动的功能分析进一步提供了数据质量的证据。这个经过注释的、多语言 fMRI 数据集促进了未来的重新分析,这些分析可以在多个感知和语言层面上解决语言处理的跨语言共性和差异的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/9424229/bbced342dae1/41597_2022_1625_Fig7_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/9424229/45856bc877e5/41597_2022_1625_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/9424229/bbced342dae1/41597_2022_1625_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/9424229/359f590d712d/41597_2022_1625_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/9424229/176c35671241/41597_2022_1625_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/9424229/df96160a2da2/41597_2022_1625_Fig3_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e71/9424229/05d1b42c19b7/41597_2022_1625_Fig5_HTML.jpg
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